TFX Pipelines

This is a TFX example pipeline.

Conceptual pipeline flow

The flow between TFX components is depicted in the graph below. The following can be said about the TFX components:

ExampleGen:
Data is ingested into the pipeline and splitted into Train and Eval sets.
StatisticsGen, SchemaGen, and ExampleValidator:
Data validation and anomalies detection.
Transform:
Transformation and preprocessing of data.
Tuner, and Trainer:
Estimator with tuned (or untuned) hyperparameters is trained.
Resolver, and Evaluator:
Model analysis of trained model. The model will be assigned BLESSED or UNBLESSED depending on the evaluation metrics threshold(s).
InfraValidator:
Model infrastructure validation. To guarantee the model is mechanically fine and prevents bad models from being pushed.
Pusher:
Model validation outcomes. If a model is deemed BLESSED it will be pushed for serving.

stateDiagram-v2
    direction LR
    [*] --> ExampleGen
    ExampleGen --> StatisticsGen
    StatisticsGen --> SchemaGen
    StatisticsGen --> ExampleValidator
    ExampleGen --> Transform
    SchemaGen --> Transform
    Transform --> Tuner
    SchemaGen --> Tuner
    SchemaGen --> Trainer
    Transform --> Trainer
    Tuner --> Trainer
    Trainer --> Resolver
    ExampleGen --> Evaluator
    Trainer --> Evaluator
    Resolver --> Evaluator
    ExampleGen --> InfraValidator
    Trainer --> InfraValidator
    Trainer --> Pusher
    Evaluator --> Pusher
    InfraValidator --> Pusher
    Pusher --> [*]

Folder structure

.
├── data/                          # Data folder
├── notebooks/                     # Example TFX notebooks
├── outputs/                       # Local runs outputs folder
├── schema/                        # Custom defined schema
├── src/
│   ├── data
│   │   └── data.csv               # Source data
│   ├── models/                    # Directory of ML model definitions
│   │   ├── estimator_model/
│   │   │   ├── constants.py       # Defines constants of the model
│   │   │   ├── model_test.py      # Model test file
│   │   │   └── model.py           # DNN model using TF estimator
│   │   ├── keras_model/
│   │   │   ├── constants.py       # Defines constants of the model
│   │   │   ├── model_test.py      # Model test file
│   │   │   └── model.py           # DNN model using Keras
│   │   ├── features_test.py
│   │   ├── features.py
│   │   ├── preprocessing_test.py  # Preprocessing test file
│   │   └── preprocessing.py       # Defines preprocessing job using TF Transform
│   ├── pipeline/                  # Directory of pipeline definition
│   │   ├── configs.py             # Defines common constants for pipeline runners
│   │   └── pipeline.py            # Defines TFX components and a pipeline
│   ├── utils/                     # Directory of utils/helper functions
│   ├── data_validation.ipynb      # Data validation notebook
│   ├── local_runner.py            # Runner for local orchestration
│   └── model_analysis.ipynb       # Model analysis notebook
├── .dockerignore
├── .gitignore
├── Dockerfile
├── requirements.in                # Environment requirements
├── requirements.txt               # Compiled requirements
└── README.md

There are some files with _test.py in their name. These are unit tests of the pipeline and it is recommended to add more unit tests as you implement your own pipelines. You can run unit tests by supplying the module name of test files with -m flag. You can usually get a module name by deleting .py extension and replacing / with .. For example:

# cd into src folder
$ cd src

# Run test file
$ python -m models.features_test

Set up environment

This pipeline is running on Python 3.8.10. In order to create an environment to run locally follow these steps:

# Make sure you have the right Python activated

# Create virtual environment
$ python -m venv .venv

# Upgrade pip
$ .venv/bin/pip install --upgrade pip

# Install requirements
$ .venv/bin/pip install -r requirements.txt

# Activate environment
$ source .venv/bin/activate

This should spin up your local environment and you should be good-to-go running the pipeline and notebook scripts locally.

Update environment

In case you need to update any requirements (e.g. update a package version or add new packages) do the following steps:

# Delete virtual environment (to make sure all old dependencies are removed)

# Make sure you have activated the right Python version and have pip-tools installed

# Update src/requirements.in with new package versions/added packages

# Compile requirements
$ pip-compile requirements.in

# Redo steps in section `Set up environment`

Run pipeline locally (python CLI)

When you want to run the pipeline using the local_runner.py script, simply run:

$ python src/local_runner.py

Run pipeline with Docker

Build the image

Build the image using the docker build command:

$ docker build -t tfx-pipeline:latest .

Run the container

Now that the image has been successfully built it is time to run a container out of it. You can do so by using the following command:

$ docker run -it \
$   -v $PWD/test_dir/data/:/app/data/ \
$   --rm tfx-pipeline:latest

janetvictorious / tfx-pipeline Goto Github PK

tfx-pipeline's Introduction

TFX Pipelines

Conceptual pipeline flow

Set up environment

Update environment

Run pipeline locally (python CLI)

Run pipeline with Docker

Build the image

Run the container

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